The paper introduces Med-Gemini, a family of highly capable multimodal models specialized for medical applications. Building on the strengths of Gemini models, Med-Gemini enhances clinical reasoning, multimodal understanding, and long-context processing capabilities. The models are fine-tuned to integrate web search for up-to-date information and can be customized for novel medical modalities using custom encoders. Med-Gemini achieves state-of-the-art (SoTA) performance on 10 out of 14 medical benchmarks, surpassing GPT-4 in direct comparisons. Notably, it outperforms prior models on the MedQA (USMLE) benchmark with an accuracy of 91.1%, a significant improvement of 4.6% over Med-PaLM 2. The models also demonstrate real-world utility by outperforming human experts in tasks such as medical text summarization and referral letter generation. Med-Gemini's long-context capabilities are further validated through tasks like needle-in-a-haystack retrieval from de-identified health records and medical video question answering. The paper highlights the potential of Med-Gemini in various medical applications, emphasizing the need for rigorous validation before real-world deployment.The paper introduces Med-Gemini, a family of highly capable multimodal models specialized for medical applications. Building on the strengths of Gemini models, Med-Gemini enhances clinical reasoning, multimodal understanding, and long-context processing capabilities. The models are fine-tuned to integrate web search for up-to-date information and can be customized for novel medical modalities using custom encoders. Med-Gemini achieves state-of-the-art (SoTA) performance on 10 out of 14 medical benchmarks, surpassing GPT-4 in direct comparisons. Notably, it outperforms prior models on the MedQA (USMLE) benchmark with an accuracy of 91.1%, a significant improvement of 4.6% over Med-PaLM 2. The models also demonstrate real-world utility by outperforming human experts in tasks such as medical text summarization and referral letter generation. Med-Gemini's long-context capabilities are further validated through tasks like needle-in-a-haystack retrieval from de-identified health records and medical video question answering. The paper highlights the potential of Med-Gemini in various medical applications, emphasizing the need for rigorous validation before real-world deployment.